Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Secrecy performance of full-duplex space-air integrated networks in the presence of active/passive eavesdropper, and friendly jammer(Wiley, 2024) Buyuksar, Ayse Betul; Erdoğan, Eylem; Altunbas, IbrahimIn this paper, a full-duplex (FD) space-air ground integrated network (SAGIN) system with passive and active eavesdroppers (PE/AE) and a friendly jammer (FJ) is investigated. The shadowing side information (SSI)-based unmanned aerial vehicle relay node (URN) selection strategy is considered to improve signal-to-interference plus noise power ratio (SINR) at the ground destination unit. To quantify the secrecy performance of the considered scenario, outage probability (OP), interception probability (IP), and transmission secrecy outage probability (TSOP) are investigated in the presence of FJ and PE/AE. The results have shown that aerial AE is an important threat since it can severely degrade the OP of the main transmission link. Furthermore, the FJ can decrease the IP of the eavesdropper by causing interference with the cost of power consumption of URNs. Simulations are performed to verify the theoretical findings.Article Citation - WoS: 9Citation - Scopus: 12Intensity and Phase Stacked Analysis of a 40-Otdr System Using Deep Transfer Learning and Recurrent Neural Networks(Optica Publishing Group, 2023) Kayan, Ceyhun Efe; Yüksel Aldoğan, Kıvılcım; Gümüş, AbdurrahmanDistributed acoustic sensors (DAS) are effective apparatuses that are widely used in many application areas for recording signals of various events with very high spatial resolution along optical fibers. To properly detect and recognize the recorded events, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks (CNNs) are highly capable tools to extract spatial information and are suitable for event recognition applications in DAS. Long short-term memory (LSTM) is an effective instrument to process sequential data. In this study, a two-stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning is proposed to classify vibrations applied to an optical fiber by a piezoelectric transducer. First, the differential amplitude and phase information is extracted from the phasesensitive optical time domain reflectometer (40-OTDR) recordings and stored in a spatiotemporal data matrix. Then, a state-of-the-art pre-trained CNN without dense layers is used as a feature extractor in the first stage. In the second stage, LSTMs are used to further analyze the features extracted by the CNN. Finally, a dense layer is used to classify the extracted features. To observe the effect of different CNN architectures, the proposed model is tested with five state-of-the-art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet, and Inception-v3). The results show that using the VGG-16 architecture in the proposed framework manages to obtain a 100% classification accuracy in 50 trainings and got the best results on the 40-OTDR dataset. The results of this study indicate that pre-trained CNNs combined with LSTM are very suitable to analyze differential amplitude and phase information represented in a spatiotemporal data matrix, which is promising for event recognition operations in DAS applications. (c) 2023 Optica Publishing GroupArticle Citation - WoS: 12Citation - Scopus: 14A Molecular Communication Perspective on Airborne Pathogen Transmission and Reception Via Droplets Generated by Coughing and Sneezing(IEEE, 2021) Güleç, Fatih; Atakan, BarışInfectious diseases spread via pathogens such as viruses and bacteria. Airborne pathogen transmission via droplets is an important mode for infectious diseases. In this paper, the spreading mechanism of infectious diseases by airborne pathogen transmission between two humans is modeled with a molecular communication perspective. An end-to-end system model which considers the pathogen-laden cough/sneeze droplets as the input and the infection state of the human as the output is proposed. This model uses the gravity, initial velocity and buoyancy for the propagation of droplets and a receiver model which considers the central part of the human face as the reception interface is proposed. Furthermore, the probability of infection for an uninfected human is derived by modeling the number of propagating droplets as a random process. The numerical results reveal that exposure time affects the probability of infection. In addition, the social distance for a horizontal cough should be at least 1.7 m and the safe coughing angle of a coughing human to infect less people should be less than -25 degrees.Article Citation - WoS: 5Citation - Scopus: 7Fast Texture Classification of Denoised Sar Image Patches Using Glcm on Spark(Türkiye Klinikleri Journal of Medical Sciences, 2020) Özcan, Caner; Ersoy, Okan; Oğul, İskender ÜlgenClassification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on patch level by using the supervised learning algorithms embedded in the Spark machine learning library. The feature vectors used as the classifier input are obtained using gray-level cooccurrence matrix which is chosen to quantitatively evaluate textural parameters and representations. SAR image patches used to construct the feature vectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimental studies were carried out using naive Bayes, decision tree, and random forest algorithms to provide comparative results, and significant accuracies were achieved. The results were also compared with a state-of-the-art deep learning method. TerraSAR-X images of high-resolution real-world SAR images were used as data.Article Citation - WoS: 5Citation - Scopus: 6Sanal Elektrik Makinaları Laboratuarı: Senkron Jeneratör Deneyleri(Gazi Üniversitesi, 2010) Bekiroğlu, Erdal; Bayrak, AlperBu çalışmada, senkron jeneratör deneylerinin bilgisayar ortamında yapılabilmesini sağlayan sanal bir elektrik makinaları laboratuar aracı geliştirilmiştir. Geliştirilen araç ile senkron jeneratörlere ait boş çalışma, kısa devre, yüklü çalışma ve paralel bağlama deneyleri yapılmaktadır. Her deney için ayrı bir deney sayfası açılarak, deneyin yapılışı, bağlantı şeması, tablo ve grafikler gösterilmektedir. C#.NET platformu kullanılarak geliştirilen sanal laboratuar aracı kullanıcı dostu olarak tasarlanmıştır. Benzetim çalışmaları için jeneratörün modeli ve pratik deneylerden yararlanılmıştır. Geliştirilen sanal laboratuar aracı, konu ile ilgili eğitim alan öğrencilerin senkron jeneratörleri daha iyi kavramasına yardımcı olacak, gerekli laboratuar donanımlarının kurulmadığı birimlerde öğrencilere bilgisayar ortamında deneyleri yapma olanağı sağlayacaktır.Article Citation - WoS: 14Citation - Scopus: 15A Droplet-Based Signal Reconstruction Approach To Channel Modeling in Molecular Communication(Institute of Electrical and Electronics Engineers Inc., 2021) Güleç, Fatih; Atakan, BarışIn this paper, a novel droplet-based signal reconstruction (SR) approach to channel modeling, which considers liquid droplets as information carriers instead of molecules in the molecular communication (MC) channel, is proposed for practical sprayer-based macroscale MC systems. These practical MC systems are significant, since they can be used in order to investigate airborne pathogen transmission with biological sensors due to the similar mechanisms of sneezing/coughing and sprayer. Our proposed approach takes a two-phase flow which is generated by the interaction of droplets in liquid phase with air molecules in gas phase into account. Two-phase flow is combined with the SR of the receiver (RX) to propose a channel model. The SR part of the model quantifies how the accuracy of the sensed molecular signal in its reception volume depends on the sensitivity response of the RX and the adhesion/detachment process of droplets. The proposed channel model is validated by employing experimental data. IEEEArticle Citation - WoS: 5Citation - Scopus: 7Signal Reconstruction in Diffusion-Based Molecular Communication(Wiley, 2019) Atakan, Barış; Güleç, FatihMolecular communication (MC) is an important nanoscale communication paradigm, which is employed for the interconnection of the nanomachines (NMs) to form nanonetworks. A transmitter NM (TN) sends the information symbols by emitting molecules into the transmission medium and a receiver NM (RN) receives the information symbols by sensing the molecule concentration. In this paper, a model of how an RN measures and reconstructs the molecular signal is proposed. The signal around the RN is assumed to be a Gaussian random process instead of the less realistic deterministic approach. After the reconstructed signal is derived as a doubly stochastic poisson process, the distortion between the signal around the RN and the reconstructed signal is derived as a new performance parameter in MC systems. The derived distortion, which is a function of system parameters such as RN radius, sampling period, and the diffusion coefficient of the channel, is shown to be valid by employing random walk simulations. Then, it is shown that the original signal can be satisfactorily reconstructed with a sufficiently low level of distortion. Finally, optimum RN design parameters, namely, RN radius, sampling period, and sampling frequency, are derived by minimizing the signal distortion. The simulation results reveal that there is a trade-off among the RN design parameters which can be jointly set for a desired signal distortion.Article Traffic Aware Cell Selection Algorithm for Tetra Trunk Based Professional Mobile Radio(Springer Verlag, 2019) Özbek, Berna; Karataş, Azad; Bardak, Erinç Deniz; Sönmez, İlkerLoad balancing and traffic management are the critical needs in cell selection decision for a better and seamless communication demands in professional mobile radios. For the cases where cell selection algorithms do not consider the traffic load, there may be call drops due to the congestion in networks or longer call setup times for the users. These undesired consequences can cause dramatic quality degradation especially for the emergency cases or public safety services. In this paper, we propose two algorithms for Tetra Trunk based professional mobile radios by considering both traffic load and received signal strength indication (RSSI) in order to reduce the significant delays while establishing transmissions. The proposed full set cell selection algorithm is prioritized to reduce the call setup time for the mobile users and the proposed reduced set cell selection algorithm is focused on minimizing the number of RSSI measurements which causes significant delay in practical professional mobile radio. We illustrate the performance results in terms of delay for Tetra Trunk based professional mobile radio.Article A Saliency-Weighted Orthogonal Regression-Based Similarity Measure for Entropic Graphs(Springer, 2019) Ergün, Aslı; Ergün, Serkan; Ünlü, Mehmet Zübeyir; Güngör, CengizVarious measures are used to determine similarity ratios among images before and after image registration. Image registration methods are based on finding the translation, rotation, and scaling parameters that maximize the similarity between two images by taking advantage of the feature points and densities that are found. While the similarity criterion is calculated, it is possible and advantageous to use approximation methods on the graphs based on information theory. The current study proposes a new similarity measure based on saliency-weighted orthogonal regression derived from the weighted sums of the saliency map of the orthogonal regression residuals formed on the entropic graph. It is evaluated in terms of both quantitative and qualitative methods and compared with other graph-based similarity measures.Article Citation - Scopus: 1Reconstruction of Geometrical and Reflection Properties of Surfaces by Using Structured Light Imaging Technique(Türkiye Klinikleri Journal of Medical Sciences, 2018) Ozan, Şükrü; Gümüştekin, ŞevketWhen a robust and dense surface reconstruction is aimed, structured light imaging techniques are usually much appreciated. In this paper we propose a method to reconstruct both geometrical and reflective properties of surfaces by using structured light imaging. We use a technique where a camera and a projector are both treated as viewing devices. They are calibrated in the same manner. Each visible point can be correctly located on both image planes without solving a correspondence problem; hence, a dense reconstruction can be obtained. Since both the camera and the projector are explicitly calibrated, lighting and viewing directions can be identified for each surface point. It is also possible to measure reflected radiance by using high dynamic range (HDR) images for each surface point. The lighting and viewing directions that are known after calibration are combined with the reflected radiance and the incoming irradiance measurements to determine the bidirectional reflectance distribution function (BRDF) values of the material at the reconstructed surface points. We illustrate the reconstruction of surface reflection properties of sample surfaces by fitting the Phong BRDF model to the BRDF measurements.